A NATION-WIDE PROJECT FOR THE A NATION-WIDE PROJECT FOR THE REVISION OF THE BELGIAN NURSING REVISION OF THE BELGIAN NURSING MINIMUM DATASET: MINIMUM DATASET:

FROM CONCEPT TO IMPLEMENTATIONFROM CONCEPT TO IMPLEMENTATION

Walter SERMEUS, PhD, RN

Katholieke Universiteit LEUVEN

BELGIUM

22nd MIC conference

Brussels

November 25, 2004

Co-authors & acknowledgementCo-authors & acknowledgement

Research team – Leuven University: W. Sermeus, L.

Delesie, K. Vanden Heede, D. Michiels– Liège University: P. Gillet, J.

Codognoto, O. Thonon, C. Van BovenCommissioned by the Ministry of

Public Health, Belgium

Nursing Minimum Data Set BelgiumNursing Minimum Data Set Belgium Compulsory from 1988 All Belgian acute Hospitals Content:

– Patient demographics– 23 nursing interventions– Nurse staffing data (FTE nurses / qualification

level) Sample: 15 days / 3 months 19 Million data recorded since 1988 Largest Nursing Dataset in the world

General rules for revisionGeneral rules for revision2002 - 20052002 - 2005

International language Integrated in hospital discharge dataset Basic + specific set per care programme: cardiology,

oncology, paediatrics, geriatrics, intensive care, chronic care, one-day clinic

Multi-purpose: reimbursement, nurse staffing, appropriateness evaluation, quality management

Minimal High user involvement

Phase I: Development of conceptual Phase I: Development of conceptual framework 2002/6 – 2002/10framework 2002/6 – 2002/10Methods:

– Literature review– Secondary data-analysis

Choice for NIC-framework:– Nursing intervention Classification

(NIC): 433 interventions in 26 classes– Comprehensive, research-based,

standardised, international accepted, available in French and Dutch

Phase II: Language developmentPhase II: Language development2002/11 – 2003/92002/11 – 2003/9 Methods:

– Panels of clinical experts (N= 75)– Six care programmes: cardiology, geriatrics,

oncology, rehabilitation, paediatrics, intensive care

– Pre-test in 15 different hospitals

Results:– Alpha version: 105 interventions– Definitions & scoring manual

Phase III: Pilot testing and tool Phase III: Pilot testing and tool validation 2003/10 – 2004/12validation 2003/10 – 2004/12 Methods:

– Data collection (dec.2003 – march 2004)– 30 days of data collection– 66 hospitals, 158 nursing wards, 95000 patient records– NMDS-I, NMDS-II, DRGs, Financial data (Finhosta),

Pharmaceutical data Results:

– Number of interventions/pat/day: Med=14 (1- 43)– Time (N= 3504: 42 hospitals, 81) wards: Med= 4’ (IQR=3’- 7’ )– Interrater reliability (9 cases, 66 raters): Above 70% for 80%

of the interventions

Validity testingValidity testing

Criterion-related validity: – comparing NMDS-II (49) with NMDS-I (19)– 21146 patient records– Correlations from 0,88 (tube feeding) to 0,16 (emotional

support) Construct validity:

– Principal component analysis (CATPCA)– Intra-class and interclass analysis– Latent variables: nursing intensity, care/cure

Face validity:– Expert panels (2004/11) are validating final NMDS-II

Interim results (2004/11)Interim results (2004/11)

– Core dataset: • Comparing between care programs• Between 25 + 39 variables

– Supplementary datasets:• Comparing within care programs• Paediatrics: 32 + 9• Intensive care: 31 + 12• Geriatrics: 36 + 5• Rehabilitation & Chronic care: 37 + 5• Cardiac care: 34 + 8• Oncology: 39 + 4• One Day clinic: 25 + 5

Phase IV: From data to applicationsPhase IV: From data to applications2004/9 – 2005/92004/9 – 2005/9 Appropriateness Evaluation protocol (AEP)

– Nursing care explains 80% of stay in hospital– In B-NMDS: 8 nursing interventions support AEP

Nurse Staffing– Validating patients’ nursing care profiles in relation to nurse

staffing ratios (N=100, 30 raters, planning march 2005)– Selection of key-interventions for nurse staffing

Reimbursement / funding– Nurse staffing & nursing care profile per DRG– 2004/10 – 2005/6

Quality Management– Nurse staffing sensitive patient outcomes (Needleman et.al.

2002)– Nurse intervention – patient problem relations (in 2005)

0%

10%

20%

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60%

70%

80%

90%

100%

C D E G H* I Sp Total

A: Medical criteria B: Nursing criteria C: Patient statusAD: Admission OVERRIDES

AEP – NMDS II

EXPLAINING APPROPRIATE STAY

Source: Gillet et.al., 2004

Linking NMDS and DRGsLinking NMDS and DRGsN days-of-stay in HDDS Nursing profile / day-of-stay

0

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6000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

*

Data: DRG 166, Coronary Artery Bypass Graft (CABG) without cardiac catherisationY2000 Belgian data, 5575 admissions, 74925 inpatient days, 2670 linked NMDS-days

Per Hospital ? For DRG166Per Hospital ? For DRG166Comparing 2 hospitals A - BComparing 2 hospitals A - B

Number

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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Number

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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Hospital A: Median LOS=9 days

Hospital B: Median LOS=12 days

Number of admission: 584/yNumber of inpatient days: 6377N in NMDS: 399 (6,2%)

Number of admission: 379/yNumber of inpatient days: 5009N in NMDS: 322 (6,4%)

115311111725192612202521222919N =

Day of Stay

20

16

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13

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9

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5

4

3

2

1

0

95%

CI R

IM1_

RC

6

5

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2

1

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NMDS – nursing profile for hospital B

N in NMDS : 322N in NMDS: 399

NMDS – nursing profile for hospital A

Relationship NMDS-profile and Relationship NMDS-profile and nurse staffing ratiosnurse staffing ratios

1 ---------------------------------------------------------------- 7

1/10

1/9

1/

8 1

/7

1/6

1/

5 ¼

1/

3 ½

1/

1 Nurses/patient/day

(selfcare Highly dependent)

Study March, 2005Delphi on NMDS-profiles (k=100, r=30)Analysing actual staffing ratios in hospitals

Phase V: From application to Phase V: From application to implementation 2005/9 – 2006/6implementation 2005/9 – 2006/6

Education Legislation Informatics Control programs Audit procedures …

Added value of NMDS-II ?Added value of NMDS-II ?

1. Updating nursing language according to evolutions in clinical practice (interventions, education, …)

2. Differentiation in core and supplementary datasets :

– Core datasets: comparing nursing care in different care programs within the organisation

– Supplementary datasets: comparing nursing care in different hospitals within a care program

3. NIC-framework: international comparisons1. International benchmarking2. Standardisation of nursing language3. Integrating NIC / NMDS in electronic patient records

Added value of NMDS-II ?Added value of NMDS-II ?

4. Integration of NMDS other datasets1. Simplification in data transmission2. Enhanced control systems

5. Linking with hospital discharge dataset1. Nursing care profiles per DRG2. New applications for funding and reimbursement

6. New applications1. E.g. Appropriate Evaluation Protocols2. Quality Monitoring

7. NMDS-II is using open framework of data collection

1. Easy update according to new technology, interventions,…

Added value of NMDS-II ?Added value of NMDS-II ?

8. NMDS-II offers a nurse staffing application1. Benchmarking nurse staffing between hospitals2. Comparing actual and required nurse staffing levels

9. NMDS-II will complement the Finhosta dataset1. Dynamic (NMDS-II) versus static (Finhosta) nurse

staffing data

10. NMDS-II is congruent to NMDS-I1. Continuity in the data2. + Wider in description and application3. + More in-depth, refined measurement